kolmogorov-arnold-networks

This paper introduces Geometric Kolmogorov-Arnold Networks (GeoKAN), a family of geometry-aware models that learn Riemannian metrics to adapt coordinates for improved function approximation and physics-informed learning.

This paper introduces the Generative Quantum-inspired Kolmogorov-Arnold Eigensolver (GQKAE), a parameter-efficient architecture that replaces traditional neural components with Kolmogorov-Arnold modules to significantly reduce memory usage and improve convergence in quantum chemistry simulations.